Join Research in Ecology and Be Part of Topical Advisory Board
2024-01-11
Evaluating Behavioural Modelling Predictions in the Blue Shark (Prionace glauca) Enables Greater Insight on Habitat Use from Location only Argos Data
Authors
-
Riley Elliott
Institute of Marine Science, Waipapa Tuamata Rau The University of Auckland, Warkworth, 0985, New Zealand
-
Jingjing Zhang
School of Biological Sciences, Waipapa Tuamata Rau The University of Auckland, Auckland, 1142, New Zealand
-
Todd Dennis
Department of Biology, School of Pure Sciences Fiji National University, P.O. Box 5529, Lautoka, Fiji
-
John Montgomery
Institute of Marine Science, Waipapa Tuamata Rau The University of Auckland, Warkworth, 0985, New Zealand
-
Craig Radford
Institute of Marine Science, Waipapa Tuamata Rau The University of Auckland, Warkworth, 0985, New Zealand
DOI:
https://doi.org/10.30564/re.v5i4.5894Abstract
The relationship between habitat and behaviour provides important information for species management. For large, free roaming, marine animals satellite tags provide high resolution information on movement, but such datasets are restricted due to cost. Extracting additional biologically important information from these data would increase utilisation and value. Several modelling approaches have been developed to identify behavioural states in tracking data. The objective of this study was to evaluate a behavioural state prediction model for blue shark (Prionace glauca) ARGOS surface location-only data. The novel nature of the six SPLASH satellite tags used enabled behavioural events to be identified in blue shark dive data and accurately mapped spatio-temporally along respective surface location-only tracks. Behavioural states modelled along the six surface location-only tracks were then tested against observed behavioural events to evaluate the model's accuracy. Results showed that the Behavioural Change Point Analysis (BCPA) model augmented with K means clustering analysis performed well for predicting foraging behaviour (correct 86% of the time). Prediction accuracy was lower for searching (52%) and travelling (63%) behaviour, likely related to the numerical dominance of foraging events in dive data. The model's validation for predicting foraging behaviour justified its application to nine additional surface location-only (SPOT tag) tracks, substantially increasing the utilisation of expensive and rare data. Results enabled the critical behavioural state of foraging, to be mapped throughout the entire home range of blue sharks, allowing drivers of critical habitat to be investigated. This validation strengthens the use of such modelling to interpret historic and future datasets, for blue sharks but also other species, contributing to conservational management.
Keywords:
Blue shark, Behavioural state modelling, Satellite tracking, Foraging behaviour, Habitat useReferences
[1] Klimley, A.P., Anderson, S.D., Pyle, P., et al., 1992. Spatiotemporal patterns of white shark (Carcharodon carcharias) predation at the South Farallon Islands, California. Copeia. 680-690.
[2] Andrzejaczek, S., Gleiss, A.C., Pattiaratchi, C.B., et al., 2019. Patterns and drivers of vertical movements of the large fishes of the epipelagic. Reviews in Fish Biology and Fisheries. 29(2), 335-354.
[3] Hays, G.C., Bailey, H., Bograd, S.J., et al., 2019. Translating marine animal tracking data into conservation policy and management. Trends in Ecology & Evolution. 34(5), 459-473.
[4] Queiroz, N., Humphries, N.E., Couto, A., et al., 2019. Global spatial risk assessment of sharks under the footprint of fisheries. Nature. 572(7770), 461-466.
[5] Sequeira, A., Hays, G., Sims, D., et al., 2019. Overhauling ocean spatial planning to improve marine megafauna conservation. Frontiers in Marine Science. 6, 639.
[6] Campana, S.E., Marks, L., Joyce, W., et al., 2006. Effects of recreational and commercial fishing on blue sharks (Prionace glauca) in Atlantic Canada, with inferences on the North Atlantic population. Canadian Journal of Fisheries and Aquatic Sciences. 63(3), 670-682.
[7] Saul, S., Brooks, E.N., Die, D., 2020. How fisher behavior can bias stock assessment: Insights from an agent-based modeling approach. Canadian Journal of Fisheries and Aquatic Sciences. 77(11), 1794-1809. DOI: https://doi.org/10.1139/cjfas-2019-0025
[8] Harcourt, R., Sequeira, A.M., Zhang, X., et al., 2019. Animal-borne telemetry: An integral component of the ocean observing toolkit. Frontiers in Marine Science. 6, 326.
[9] Andrzejaczek, S., Lucas, T.C., Goodman, M.C., et al., 2022. Diving into the vertical dimension of elasmobranch movement ecology. Science Advances. 8(33), eabo1754.
[10] Druon, J.N., Campana, S., Vandeperre, F., et al., 2022. Global-scale environmental niche and habitat of blue shark (Prionace glauca) by size and sex: A pivotal step to improving stock management. Frontiers in Marine Science. 9, 828412.
[11] Renshaw, S., Hammerschlag, N., Gallagher, A.J., et al., 2023. Global tracking of shark movements, behaviour and ecology: A review of the renaissance years of satellite tagging studies, 2010-2020. Journal of Experimental Marine Biology and Ecology. 560, 151841.
[12] Nakano, H., Stevens, J., 2008. The biology and ecology of the blue shark Prionace glauca. Sharks of the open ocean: Biology, fisheries and conservation. Blackwell Publishing: Oxford, UK. pp. 140-148.
[13] Clarke, S.C., Magnussen, J.E., Abercrombie, D.L., et al., 2006. Identification of shark species composition and proportion in the Hong Kong shark fin market based on molecular genetics and trade records. Conservation Biology. 20(1), 201-211.
[14] Clarke, S.C., McAllister, M.K., Milner‐Gulland, E.J., et al., 2006. Global estimates of shark catches using trade records from commercial markets. Ecology Letters. 9(10), 1115-1126.
[15] Okes, N., Sant, G., 2019. An overview of major shark traders, catchers and species. TRAFFIC: Cambridge UK.
[16] Simpfendorfer, C.A., Hueter, R.E., Bergman, U., et al., 2002. Results of a fishery-independent survey for pelagic sharks in the western North Atlantic, 1977-1994. Fisheries Research. 55(1-3), 175-192.
[17] Baum, J.K., Myers, R.A., Kehler, D.G., et al., 2003. Collapse and conservation of shark populations in the Northwest Atlantic. Science. 299(5605), 389-392.
[18] Ward, P., Myers, R.A., 2005. Shifts in open‐ocean fish communities coinciding with the commencement of commercial fishing. Ecology. 86(4), 835-847.
[19] Kleiber, P., Clarke, S., Bigelow, K., et al., 2009. North Pacific Blue Shark Stock Assessment [Internet]. Available from: https://repository.library.noaa.gov/view/noaa/3676
[20] Baum, J.K., Blanchard, W., 2010. Inferring shark population trends from generalized linear mixed models of pelagic longline catch and effort data. Fisheries Research. 102(3), 229-239.
[21] Queiroz, N., Humphries, N.E., Mucientes, G., et al., 2016. Ocean-wide tracking of pelagic sharks reveals extent of overlap with longline fishing hotspots. Proceedings of the National Academy of Sciences. 113(6), 1582-1587.
[22] Dulvy, N.K., Pacoureau, N., Rigby, C.L., et al., 2021. Overfishing drives over one-third of all sharks and rays toward a global extinction crisis. Current Biology. 31(21), 4773-4787.
[23] Aires-da-Silva, A., Ferreira, R.L., Pereira, J.G., 2008. Case study: Blue shark catch-rate patterns from the Portuguese swordfish longline fishery in the Azores. Sharks of the open ocean: Biology, fisheries and conservation. Blackwell Publishing: Oxford. pp. 230-235.
[24] Teo, S.L., Boustany, A., Blackwell, S., et al., 2004. Validation of geolocation estimates based on light level and sea surface temperature from electronic tags. Marine Ecology Progress Series. 283, 81-98.
[25] Moyes, C.D., Fragoso, N., Musyl, M.K., et al., 2006. Predicting postrelease survival in large pelagic fish. Transactions of the American Fisheries Society. 135(5), 1389-1397.
[26] Campana, S.E., Joyce, W., Manning, M.J., 2009. Bycatch and discard mortality in commercially caught blue sharks Prionace glauca assessed using archival satellite pop-up tags. Marine Ecology Progress Series. 387, 241-253.
[27] Campana, S.E., Dorey, A., Fowler, M., et al., 2011. Migration pathways, behavioural thermoregulation and overwintering grounds of blue sharks in the Northwest Atlantic. PloS One. 6(2), e16854. DOI: https://doi.org/10.1371/journal.pone.0016854
[28] Campana, S.E., Joyce, W., Fowler, M., et al., 2016. Discards, hooking, and post-release mortality of porbeagle (Lamna nasus), shortfin mako (Isurus oxyrinchus), and blue shark (Prionace glauca) in the Canadian pelagic longline fishery. ICES Journal of Marine Science. 73(2), 520-528.
[29] Rogers, P.J., Huveneers, C., Page, B., et al., 2009. Movement Patterns of Pelagic Sharks in the Southern and Indian Oceans: Determining Critical Habitats and Migration Paths [Internet]. Available from: https://www.researchgate.net/publication/255608343_Movement_patterns_of_pelagic_sharks_in_the_Southern_and_Indian_Oceans_determining_critical_habitats_and_migration_paths
[30] Queiroz, N., Humphries, N.E., Noble, L.R., et al., 2010. Short-term movements and diving behaviour of satellite-tracked blue sharks Prionace glauca in the northeastern Atlantic Ocean. Marine Ecology Progress Series. 406, 265-279.
[31] Stevens, J.D., Bradford, R.W., West, G.J., 2010. Satellite tagging of blue sharks (Prionace glauca) and other pelagic sharks off eastern Australia: Depth behaviour, temperature experience and movements. Marine Biology. 157, 575-591.
[32] Block, B.A., Jonsen, I.D., Jorgensen, S.J., et al., 2011. Tracking apex marine predator movements in a dynamic ocean. Nature. 475(7354), 86-90.
[33] Musyl, M.K., Brill, R., Curran, D.S., et al., 2011. Postrelease survival, vertical and horizontal movements, and thermal habitats of five species of pelagic sharks in the central Pacific Ocean. Fishery Bulletin. 109(4), 341.
[34] Vandeperre, F., Aires-da-Silva, A., Santos, M., et al., 2014. Demography and ecology of blue shark (Prionace glauca) in the central North Atlantic. Fisheries Research. 153, 89-102.
[35] Carvalho, F., Ahrens, R., Murie, D., et al., 2015. Using pop-up satellite archival tags to inform selectivity in fisheries stock assessment models: A case study for the blue shark in the South Atlantic Ocean. ICES Journal of Marine Science. 72(6), 1715-1730.
[36] Doyle, T.K., Bennison, A., Jessopp, M., et al., 2015. A dawn peak in the occurrence of ‘knifing behaviour’ in blue sharks. Animal Biotelemetry. 3, 1-6.
[37] Howey, L.A., Wetherbee, B.M., Tolentino, E.R., et al., 2017. Biogeophysical and physiological processes drive movement patterns in a marine predator. Movement Ecology. 5(1), 1-16.
[38] Musyl, M.K., Gilman, E.L., 2018. Post-release fishing mortality of blue (Prionace glauca) and silky shark (Carcharhinus falciformes) from a Palauan-based commercial longline fishery. Reviews in Fish Biology and Fisheries. 28(3), 567-586.
[39] Braun, C.D., Gaube, P., Sinclair-Taylor, T.H., et al., 2019. Mesoscale eddies release pelagic sharks from thermal constraints to foraging in the ocean twilight zone. Proceedings of the National Academy of Sciences. 116(35), 17187-17192.
[40] Maxwell, S.M., Scales, K.L., Bograd, S.J., et al., 2019. Seasonal spatial segregation in blue sharks (Prionace glauca) by sex and size class in the Northeast Pacific Ocean. Diversity and Distributions. 25(8), 1304-1317.
[41] Nosal, A.P., Cartamil, D.P., Wegner, N.C., et al., 2019. Movement ecology of young-of-the-year blue sharks Prionace glauca and shortfin makos Isurus oxyrinchus within a putative binational nursery area. Marine Ecology Progress Series. 623, 99-115.
[42] Coelho, R., Macías, D., de Urbina, J.O., et al., 2020. Local indicators for global species: Pelagic sharks in the tropical northeast Atlantic, Cabo Verde islands region. Ecological Indicators. 110, 105942.
[43] Elliott, R.G., Montgomery, J.C., Della Penna, A., et al., 2022. Satellite tags describe movement and diving behaviour of blue sharks Prionace glauca in the southwest Pacific. Marine Ecology Progress Series. 689, 77-94.
[44] Fujinami, Y., Kurashima, A., Shiozaki, K., et al., 2022. New insights into spatial segregation by sex and life-history stage in blue sharks Prionace glauca in the northwestern Pacific. Marine Ecology Progress Series. 696, 69-84.
[45] Patterson, T.A., Basson, M., Bravington, M.V., et al., 2009. Classifying movement behaviour in relation to environmental conditions using hidden Markov models. Journal of Animal Ecology. 78(6), 1113-1123.
[46] Charnov, E.L., 1976. Optimal foraging, the marginal value theorem. Theoretical Population Biology. 9(2), 129-136.
[47] Nathan, R., Getz, W.M., Revilla, E., et al., 2008. A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences. 105(49), 19052-19059. DOI: https://doi.org/10.1073/pnas.0800375105
[48] Ritz, D.A., Hobday, A.J., Montgomery, J.C., et al., 2011. Social aggregation in the pelagic zone with special reference to fish and invertebrates. Advances in Marine Biology. 60, 161-227.
[49] Turchin, P., Odendaal, F.J., Rausher, M.D., 1991. Quantifying insect movement in the field. Environmental Entomology. 20(4), 955-963.
[50] Barraquand, F., Benhamou, S., 2008. Animal movements in heterogeneous landscapes: Identifying profitable places and homogeneous movement bouts. Ecology. 89(12), 3336-3348.
[51] Bowler, D.E., Benton, T.G., 2005. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biological Reviews. 80(2), 205-225.
[52] Fauchald, P., Tveraa, T., 2003. Using first‐passage time in the analysis of area‐restricted search and habitat selection. Ecology. 84(2), 282-288.
[53] Sims, D.W., Southall, E.J., Humphries, N.E., et al., 2008. Scaling laws of marine predator search behaviour. Nature. 451(7182), 1098-1102.
[54] Gurarie, E., Andrews, R.D., Laidre, K.L., 2009. A novel method for identifying behavioural changes in animal movement data. Ecology Letters. 12(5), 395-408.
[55] Van Moorter, B., Visscher, D.R., Jerde, C.L., et al., 2010. Identifying movement states from location data using cluster analysis. The Journal of Wildlife Management. 74(3), 588-594.
[56] Robinson, P.W., Tremblay, Y., Crocker, D.E., et al., 2007. A comparison of indirect measures of feeding behaviour based on ARGOS tracking data. Deep Sea Research Part II: Topical Studies in Oceanography. 54(3-4), 356-368.
[57] Browning, E., Bolton, M., Owen, E., et al., 2018. Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds. Methods in Ecology and Evolution. 9(3), 681-692.
[58] Florko, K.R., Shuert, C.R., Cheung, W.W., et al., 2023. Linking movement and dive data to prey distribution models: New insights in foraging behaviour and potential pitfalls of movement analyses. Movement Ecology. 11(1), 17.
[59] Zhang, J., O’Reilly, K.M., Perry, G.L., et al., 2015. Extending the functionality of behavioural change-point analysis with k-means clustering: a case study with the little penguin (Eudyptula minor). PloS One. 10(4), e0122811. DOI: https://doi.org/10.1371/journal.pone.0122811
[60] Gurarie, E., 2008. Models and Analysis of Animal Movements: From Individual Tracks to Mass Dispersal [Internet]. Available from: https://experts.esf.edu/esploro/outputs/99917678904826?institution=01SUNY_ESF&skipUsageReporting=true&recordUsage=false
[61] ESRI. ArcMap. Redlands, California: ESRI (Environmental Systems Resource Institute); 2013. Available from: https://www.esri.com/en-us/home
[62] The R Project for Statistical Computing [Internet]. Available from: https://www.R-project.org/
[63] Allain, V., Kerandel, J.A., Andréfouët, S., et al., 2008. Enhanced seamount location database for the western and central Pacific Ocean: Screening and cross-checking of 20 existing datasets. Deep Sea Research Part I: Oceanographic Research Papers. 55(8), 1035-1047.
[64] Benhamou, S., 1992. Efficiency of area-concentrated searching behaviour in a continuous patchy environment. Journal of Theoretical Biology. 159(1), 67-81.
[65] Mori, Y., Watanabe, Y., Mitani, Y., et al., 2005. A comparison of prey richness estimates for Weddell seals using diving profiles and image data. Marine Ecology Progress Series. 295, 257-263.
[66] Bailey, H., Lyubchich, V., Wingfield, J., et al., 2019. Empirical evidence that large marine predator foraging behavior is consistent with area‐restricted search theory. Ecology. 100(8), e02743.
[67] McMahon, C.R., Hindell, M.A., Charrassin, J.B., et al., 2019. Finding mesopelagic prey in a changing Southern Ocean. Scientific Reports. 9(1), 19013.
[68] Sims, D.W., Southall, E.J., Tarling, G.A., et al., 2005. Habitat-specific normal and reverse diel vertical migration in the plankton-feeding basking shark. Journal of Animal Ecology. 74, 755-761.
[69] Shepard, E.L., Ahmed, M.Z., Southall, E.J., et al., 2006. Diel and tidal rhythms in diving behaviour of pelagic sharks identified by signal processing of archival tagging data. Marine Ecology Progress Series. 328, 205-213.
[70] Gleiss, A.C., Norman, B., Wilson, R.P., 2011. Moved by that sinking feeling: Variable diving geometry underlies movement strategies in whale sharks. Functional Ecology. 25(3), 595-607.
[71] Shiels, H.A., Galli, G.L.J., Block, B.A., 2015. Cardiac function in an endothermic fish: cellular mechanisms for overcoming acute thermal challenges during diving. Proceedings of the Royal Society B: Biological Sciences. 282(1800), 20141989.
[72] Zimmer, W.M., Johnson, M.P., D’Amico, A., et al., 2003. Combining data from a multisensor tag and passive sonar to determine the diving behavior of a sperm whale (Physeter macrocephalus). IEEE Journal of Oceanic Engineering. 28(1), 13-28.
[73] Richer de Forges, B., Koslow, J.A., Poore, G.C.B., 2000. Diversity and endemism of the benthic seamount fauna in the southwest Pacific. Nature. 405(6789), 944-947.
[74] Worm, B., Lotze, H.K., Myers, R.A., 2003. Predator diversity hotspots in the blue ocean. Proceedings of the National Academy of Sciences. 100(17), 9884-9888.
[75] Clark, M., 1999. Fisheries for orange roughy (Hoplostethus atlanticus) on seamounts in New Zealand. Oceanologica Acta. 22(6), 593-602.
[76] Schmidt, S., Christiansen, S., 2004. The Offshore MPA Toolbox: Implementing Marine Protected Areas in the North-east Atlantic Offshore: Seamounts—A Case Study [Internet]. Available from: https://epic.awi.de/id/eprint/37314/18/OASIS_Offshore_Toolbox.pdf
[77] Davies, A.J., Roberts, J.M., Hall-Spencer, J., 2007. Preserving deep-sea natural heritage: Emerging issues in offshore conservation and management. Biological Conservation. 138(3-4), 299-312.
[78] Litvinov, F., 2007. Fish visitors to seamounts: Aggregations of large pelagic sharks above seamounts. Seamounts: Ecology, fisheries & conservation. Blackwell Publishing Ltd: Oxford, UK. DOI: https://doi.org/10.1002/9780470691953.ch10b
[79] Williams, R.G., Follows, M.J., 1998. Eddies make ocean deserts bloom. Nature. 394(6690), 228-229.
[80] McGregor, V., Horn, P.L., 2015. Factors Affecting the Distribution of Highly Migratory Species in New Zealand Waters [Internet]. Available from: https://www.mpi.govt.nz/dmsdocument/5575-aebr-146-factors-affecting-the-distribution-of-highly-migratory-species-in-new-zealand-waters
[81] Genin, A., Boehlert, G.W., 1985. Dynamics of temperature and chlorophyll structures above a seamount: an oceanic experiment. Journal of Marine Research. 43(4), 907-924.
[82] Dower, J., Freeland, H., Juniper, K., 1992. A strong biological response to oceanic flow past Cobb Seamount. Deep Sea Research Part A. Oceanographic Research Papers. 39(7-8), 1139-1145.
[83] Mouriño, B., Fernández, E., Serret, P., et al., 2001. Variability and seasonality of physical and biological fields at the Great Meteor Tablemount (subtropical NE Atlantic). Oceanologica Acta. 24(2), 167-185.
[84] Queiroz, N., Humphries, N.E., Noble, L.R., et al., 2012. Spatial dynamics and expanded vertical niche of blue sharks in oceanographic fronts reveal habitat targets for conservation. PloS One. 7(2), e32374.
[85] Carey, F.G., Scharold, J.V., Kalmijn, A.J., 1990. Movements of blue sharks (Prionace glauca) in depth and course. Marine Biology. 106, 329-342.
[86] Carvalho, F.C., Murie, D.J., Hazin, F.H., et al., 2011. Spatial predictions of blue shark (Prionace glauca) catch rate and catch probability of juveniles in the Southwest Atlantic. ICES Journal of Marine Science. 68(5), 890-900.
[87] Selles, J., Sabarros, P.S., Romanov, E., et al., 2014. Characterisation of Blue Shark (Prionace glauca) Hotspots in the South-West Indian Ocean [Internet]. IOTC–2014–WPEB10–23. Available from: https://www.researchgate.net/publication/328199609_Characterisation_of_blue_shark_Prionace_glauca_hotspots_in_the_South-West_Indian_Ocean
Downloads
How to Cite
Elliott, R., Zhang, J., Dennis, T., Montgomery, J., & Radford, C. (2023). Evaluating Behavioural Modelling Predictions in the Blue Shark (Prionace glauca) Enables Greater Insight on Habitat Use from Location only Argos Data. Research in Ecology, 5(4), 13–30. https://doi.org/10.30564/re.v5i4.5894
Issue
Article Type
Articles
License
Copyright © 2023 Riley Elliott, Jingjing Zhang, Todd Dennis, John Montgomery, Craig Radford
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
